String Algorithms and Pattern Matching Questions

Covers algorithmic techniques and practical skills for solving string problems and pattern matching tasks. Core algorithm knowledge includes substring search and pattern matching algorithms such as Knuth Morris Pratt, Rabin Karp, Boyer Moore, Z algorithm, Aho Corasick for multiple pattern matching, and rolling hash methods. Data structures and suffix structures are important, including tries, suffix arrays, suffix trees, and suffix automata, together with longest common prefix arrays and related construction techniques. Also includes dynamic programming approaches for string problems such as edit distance and longest common subsequence, palindrome and anagram detection methods, and regular expression concepts and engine behavior. Emphasizes algorithmic complexity analysis, time and space trade offs, memory and streaming constraints, and optimization strategies for very long inputs and high throughput text processing. Practical considerations include parsing and string manipulation idioms in common languages, Unicode and character encoding issues, edge case handling, test case design for strings, and real world applications such as log analysis, text search, and data transformation.

HardTechnical

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Rabin-Karp's rolling hash can be attacked by adversarial inputs crafted to produce collisions. Describe how an attacker can degrade performance and propose mitigations such as randomized base/modulus selection, double hashing, fallback deterministic checks, or cryptographic hashing. Discuss trade-offs between robustness and performance.

HardTechnical

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Given k input strings with total length N, describe an algorithm using a generalized suffix array plus LCP array to find the longest substring common to all k strings. Outline building the generalized SA, annotating suffixes by source string, and scanning LCP intervals to find the maximal common substring across all k origins.

HardTechnical

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Given a precomputed suffix automaton (SAM) for string s, describe how to precompute end-pos counts for each state so you can answer queries of the form: how many times does pattern p occur in s? Explain precomputation (topological order by maxlen), how to answer queries, and complexity.

MediumTechnical

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Write a Python function z_search(text: str, pattern: str) -> List[int] that finds all occurrences of pattern using the Z algorithm by computing Z on pattern + '$' + text. Implement the Z-array computation in O(n) time and return starting positions in text where pattern matches.

MediumTechnical

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Given a suffix array SA for string s, implement Kasai's algorithm in Python to compute the LCP array in O(n) time. Explain how the inverse suffix array and the reuse of previously computed LCP (k-1) for adjacent suffixes gives linear time behavior.

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